Palmer Drought Severity Index

Correlation of PDSI monthly averages for the Agencies

• All the agencies have a high PDSI correlation

Correlation between Chuska monthly average swe and the monthly average PDSI of the agencies

• not a very high correlation

## Warning: Removed 95 rows containing missing values (position_stack).

## Warning: Removed 95 rows containing missing values (position_stack).

## 
##  Pearson's product-moment correlation
## 
## data:  chuska_fd$anomaly_perc and chuska_fd$anomaly_pdsi
## t = 1.9047, df = 94, p-value = 0.05988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.008029559  0.378620112
## sample estimates:
##       cor 
## 0.1927667

PDSI Whole Navajo Nation

## 
## Call:
## lm(formula = nn_mnth_pdsi$pdsi ~ nn_mnth_pdsi$date)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8867 -1.2088 -0.0772  1.2816  5.0680 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.414e+00  2.046e-01   6.908 1.55e-11 ***
## nn_mnth_pdsi$date -1.455e-04  1.779e-05  -8.179 2.55e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.67 on 483 degrees of freedom
## Multiple R-squared:  0.1216, Adjusted R-squared:  0.1198 
## F-statistic: 66.89 on 1 and 483 DF,  p-value: 2.552e-15

Compare PDSI whole NN to total swe for all high elevation regions

## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.

## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.

Now let’s looks at correlations between different months

Notation:

• Winter SWE: November - February (w)
• Spring SWE: March - April (sp)
• Early summer PDSI: May - June (esu)
• Mid summer PDSI: June - July (msu)
• Late summer PDSI: July - August (lsu)

Compare SWE of Chuska to PDSI watersheds

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   waterYear = col_integer(),
##   swe_mm = col_double()
## )

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   pdsi = col_double()
## )
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   pdsi = col_double()
## )
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   pdsi = col_double()
## )
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   pdsi = col_double()
## )

Watershed PDSI timeseries (1981-present)

Chuska SWE Anomalies vs watershed PDSI

Compare Chuska SWE different months of SWE to different months of PDSI

Winter SWE Chuskas and AMJ PDSI watersheds

• generally pretty low positive correlations between winter swe and AMJ PDSI

Mid San Juan and Chuska have the highest correlation of winter swe and AMJ PDSI. Let’s check by year

• 3/8 negative swe anomaly years followed by negative pdsi AMJ
• 5/8 positive swe anomaly years followed by positive pdsi AMJ

Compare SWE of Chuska to PDSI chapters of interest

## 
## Call:
## lm(formula = tsaile_pdsi_mnth$pdsi ~ tsaile_pdsi_mnth$date)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1174 -1.3595  0.0247  1.4272  4.6402 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.588e+00  2.181e-01   7.282 1.34e-12 ***
## tsaile_pdsi_mnth$date -1.714e-04  1.894e-05  -9.053  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.783 on 484 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1448, Adjusted R-squared:  0.143 
## F-statistic: 81.95 on 1 and 484 DF,  p-value: < 2.2e-16

Chuska SWE Anomalies vs Tsaile chapter PDSI

Chuska Average SWE cold season vs PDSI of AMJ of Tsaile